Which integrations are available for this server?

Uses OpenAI's embedding API for semantic search capabilities within the knowledge base, enabling hybrid search that combines keyword and semantic retrieval techniques. Provides persistent storage for ingested documents and vector embeddings, maintaining a queryable knowledge base across server restarts.

How do I use Antigravity PDF MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Antigravity PDF MCP Server search for information about smart chunking and hybrid search techniques" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Antigravity PDF MCP Server

A powerful Model Context Protocol (MCP) server that enables intelligent document ingestion and querying capabilities for AI agents and IDEs. This server allows you to build a persistent knowledge base from PDFs, Markdown, and Text files, and query them using advanced hybrid search techniques.

Features

Multi-Format Ingestion: Support for .pdf, .md, and .txt files.
Smart Chunking: Recursive character splitting preserves document structure (paragraphs, headers).
Persistent Storage: Uses SQLite (antigravity.db) to store documents and vectors across restarts.
Advanced Retrieval:
- Hybrid Search: Combines TF-IDF (keyword) and OpenAI Embeddings (semantic) using Reciprocal Rank Fusion (RRF).
- Filtering: Scope searches to specific documents.
- Citations: Returns page numbers (e.g., [Page 5]) for easy verification.
User Experience: Real-time progress notifications during ingestion.
MCP Protocol: Fully compliant with the Model Context Protocol over Stdio.

Prerequisites

Node.js (v18 or higher)
npm

Installation

Clone the repository:
git clone <repository-url> cd antigravity-pdf-mcp
Install dependencies:
npm install
Build the project:
npm run build

Configuration

To enable Semantic Search (Embeddings), create a .env file in the root directory:

OPENAI_API_KEY=sk-your-api-key-here

If no API key is provided, the server will fallback to local TF-IDF search only.

Tools

The server exposes the following MCP tools:

ingest_document: Ingest a file (PDF, TXT, MD) into the knowledge base.
- path: Absolute path to the file.
query_knowledge_base: Search the knowledge base.
- query: The search query.
- document_id (Optional): Filter results to a specific document ID.
list_documents: List all ingested documents.
reset_library: Clear the entire database.
ingest_pdf (Deprecated): Alias for ingest_document.

Usage with IDEs

This server uses the Stdio transport, making it compatible with any MCP-compliant client or IDE.

Antigravity IDE

Open Settings > MCP Servers.
Click Add Server.
Configure the server:
- Name: antigravity-pdf
- Command: node
- Arguments:
  /absolute/path/to/antigravity-pdf-mcp/dist/server.js
- Environment Variables:
  - OPENAI_API_KEY: Your OpenAI API key.

VSCode / Claude Desktop

Add to your MCP configuration file (e.g., claude_desktop_config.json):

{ "mcpServers": { "antigravity-pdf": { "command": "node", "args": [ "/absolute/path/to/antigravity-pdf-mcp/dist/server.js" ], "env": { "OPENAI_API_KEY": "your-api-key-if-needed" } } } }

Contributing

Fork & Clone: Clone your fork locally.
Branch: Create a feature branch (git checkout -b feature/amazing-feature).
Develop: Make your changes.
Verify:
- Run npm run build to check for errors.
- Use npx ts-node verify_ux.ts to test ingestion and retrieval.
Commit & Push: Push changes to your fork.
Pull Request: Open a PR against the main repository.